matric

In matric: Metrics for Similarity Matrices

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Load packages

suppressMessages(suppressWarnings(library(dplyr)))
suppressMessages(suppressWarnings(library(ggplot2)))
suppressMessages(suppressWarnings(library(magrittr)))
suppressMessages(suppressWarnings(library(matric)))

Calculate similarity

sim_df <- matric::sim_calculate(cellhealth)

Filter and collate similarity

drop_group <-
  data.frame(Metadata_gene_name = "EMPTY")

reference <-
  data.frame(Metadata_gene_name = c("Chr2"))

all_same_cols_ref <-
  c(
    "Metadata_cell_line",
    "Metadata_Plate"
  )

all_same_cols_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

all_same_cols_rep_ref <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name",
    "Metadata_Plate"
  )

any_different_cols_non_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

all_same_cols_non_rep <-
  c(
    "Metadata_cell_line",
    "Metadata_Plate"
  )

all_different_cols_non_rep <-
  c("Metadata_gene_name")

all_same_cols_group <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name"
  )

any_different_cols_group <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

annotation_cols <-
  c(
    "Metadata_cell_line",
    "Metadata_gene_name",
    "Metadata_pert_name"
  )

collated_sim <-
  matric::sim_collate(
    sim_df,
    reference = reference,
    all_same_cols_rep = all_same_cols_rep,
    all_same_cols_rep_ref = all_same_cols_rep_ref,
    all_same_cols_ref = all_same_cols_ref,
    any_different_cols_non_rep = any_different_cols_non_rep,
    all_same_cols_non_rep = all_same_cols_non_rep,
    all_different_cols_non_rep = all_different_cols_non_rep,
    any_different_cols_group = any_different_cols_group,
    all_same_cols_group = all_same_cols_group,
    annotation_cols = annotation_cols,
    drop_group = drop_group
  )

Compute metrics

metrics <-
  matric::sim_metrics(collated_sim, "ref", calculate_grouped = TRUE)

Plot metrics

Per row (Level 0)

ggplot(
  metrics$level_1_0,
  aes(sim_scaled_mean_ref_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "bottom", legend.box = "horizontal")

Per set (Level 1-0)

ggplot(
  metrics$level_1,
  aes(sim_scaled_mean_ref_i_mean_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "bottom", legend.box = "horizontal")

Per group (Level 2)

ggplot(
  metrics$level_2_1,
  aes(sim_scaled_mean_ref_g, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "bottom", legend.box = "horizontal")

Plot metrics from precomputed data

Per row (Level 0)

ggplot(
  cellhealthmetrics$level_1_0,
  aes(sim_scaled_mean_ref_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "none")

Per set (Level 1-0)

ggplot(
  cellhealthmetrics$level_1,
  aes(sim_scaled_mean_ref_i_mean_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "none")

ggplot(
  cellhealthmetrics$level_1_0,
  aes(sim_retrieval_average_precision_ref_i, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .01) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "none")

Per group (Level 2)

ggplot(
  cellhealthmetrics$level_2_1,
  aes(sim_scaled_mean_ref_g, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .1) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "none")

ggplot(
  cellhealthmetrics$level_2_1,
  aes(sim_retrieval_average_precision_ref_g, fill = Metadata_gene_name)
) +
  geom_histogram(binwidth = .02) +
  facet_wrap(~Metadata_cell_line) +
  theme(legend.position = "none")

Calculate similarity in a lazy manner and evaluate metrics

sim_df_lazy <-
  matric::sim_calculate(
    cellhealth,
    lazy = TRUE
  )

collated_sim_lazy <-
  matric::sim_collate(
    sim_df_lazy,
    reference = reference,
    all_same_cols_rep = all_same_cols_rep,
    all_same_cols_rep_ref = all_same_cols_rep_ref,
    all_same_cols_ref = all_same_cols_ref,
    any_different_cols_non_rep = any_different_cols_non_rep,
    all_same_cols_non_rep = all_same_cols_non_rep,
    all_different_cols_non_rep = all_different_cols_non_rep,
    any_different_cols_group = any_different_cols_group,
    all_same_cols_group = all_same_cols_group,
    annotation_cols = annotation_cols,
    drop_group = drop_group
  )

collated_sim_lazy <-
  sim_calculate_ij(cellhealth, collated_sim_lazy)

metrics_lazy <-
  matric::sim_metrics(collated_sim_lazy, "ref", calculate_grouped = TRUE)

all.equal(
  metrics_lazy,
  metrics
)

Calculate similarity in a lazy, optimized manner and evaluate metrics

The optimizations are useful only if you are not calculating non-replicate metrics and if the group replicates are not computed on references

sim_df_optimized_lazy <-
  matric::sim_calculate(
    cellhealth,
    lazy = TRUE,
    all_same_cols_rep_or_group = all_same_cols_group,
    all_same_cols_rep_ref = all_same_cols_rep_ref,
    all_same_cols_ref = all_same_cols_ref,
    reference = reference
  )

collated_sim_optimized_lazy <-
  matric::sim_collate(
    sim_df_optimized_lazy,
    reference = reference,
    all_same_cols_rep = all_same_cols_rep,
    all_same_cols_rep_ref = all_same_cols_rep_ref,
    all_same_cols_ref = all_same_cols_ref,
    any_different_cols_non_rep = NULL,
    all_same_cols_non_rep = NULL,
    all_different_cols_non_rep = NULL,
    any_different_cols_group = NULL,
    all_same_cols_group = NULL,
    annotation_cols = annotation_cols,
    drop_group = drop_group
  )

collated_sim_optimized_lazy <-
  sim_calculate_ij(cellhealth, collated_sim_optimized_lazy)

metrics_optimized_lazy <-
  matric::sim_metrics(
    collated_sim_optimized_lazy, "ref",
    calculate_grouped = FALSE
  )

all.equal(
  metrics_optimized_lazy$level_1_0,
  metrics$level_1_0
)

all.equal(
  metrics_optimized_lazy$level_1,
  metrics$level_1
)

matric documentation built on April 1, 2023, 12:19 a.m.